Detection of Copy-move Image forgery using SVD and Cuckoo Search Algorithm
This addresses the issue of parameter sensitivity in forgery detection for digital image forensics, though it appears incremental as it builds on prior SVD techniques.
The paper tackled the problem of copy-move image forgery detection by proposing a novel approach that integrates the Cuckoo Search algorithm with Singular Value Decomposition to generate customized parameters, resulting in improved detection accuracy compared to existing SVD-based methods.
Copy-move forgery is one of the simple and effective operations to create forged images. Recently, techniques based on singular value decomposition (SVD) are widely used to detect copy-move forgery (CMF). Some approaches based on SVD are most acceptable to detect copy-move forgery but some copy-move forgery detection approaches can not produce satisfactory detection results. Sometimes these approaches may even produce error results. According to our observation, detection result produced using SVD depend highly on those parameters whose values are often determined with experiences. These values are only applicable to a few images, which limit their application. To solve this problem, a novel approach named as copy-move forgery detection using Cuckoo search algorithm (CMFD-CS) is proposed in this paper. CMFD-CS integrates the CS algorithm into SVD. It utilizes the CS algorithm to generate customized parameter values for images, which are used CMFD under block-based framework.